Small non-coding RNA landscape of extracellular vesicles from a post-traumatic model of equine osteoarthritis

Extracellular vesicles comprise an as yet inadequately investigated intercellular communication pathway in the field of early osteoarthritis. We hypothesised that the small non-coding RNA expression pattern in synovial fluid and plasma would change during progression of experimental osteoarthritis. In this study, we conducted small RNA sequencing to provide a comprehensive overview of the temporal expression profiles of small non-coding transcripts carried by extracellular vesicles derived from plasma and synovial fluid for the first time in a posttraumatic model of equine osteoarthritis. Additionally, we characterised synovial fluid and plasma-derived extracellular vesicles with respect to quantity, size, and surface markers. The different temporal expressions of seven microRNAs in plasma and synovial fluid-derived extracellular vesicles, eca-miR-451, eca-miR-25, eca-miR-215, eca-miR-92a, eca-miR-let-7c, eca-miR-486-5p, and eca-miR-23a, and four snoRNAs, U3, snord15, snord46, and snord58, represent potential biomarkers for early osteoarthritis. Bioinformatics analysis of the differentially expressed microRNAs in synovial fluid highlighted that in early osteoarthritis these related to the inhibition of cell cycle, cell cycle progression, DNA damage and cell proliferation as well as increased cell viability and differentiation of stem cells. Plasma and synovial fluid-derived extracellular vesicle small non-coding signatures have been established for the first time in a temporal model of osteoarthritis. These could serve as novel biomarkers for evaluation of osteoarthritis progression or act as potential therapeutic targets.

Introduction
Osteoarthritis (OA) is a degenerative joint disease characterised by deterioration of articular cartilage and accompanied by changes in the bone and soft tissues of the joint (1), which adversely impact the health of equine athletes. It is a major welfare issue resulting in substantial morbidity and mortality (2). Lameness resulting from OA is a . /fvets. . major cause of poor performance and early retirement (3). Despite the huge welfare importance of OA, our understanding of pathophysiological mechanisms involved is limited (4). OA is characterised by increase in cartilage extracellular matrix (ECM) degradation by proteases and reduction in ECM production (5). We recently identified that differential expression (DE) of small nucleolar RNAs (snoRNAs) contributes to this imbalance, a key mechanism in OA (6,7). We require biomarkers to identify early OA before cartilage ECM is irreversibly degraded, and our group has identified small non-coding RNAs (sncRNAs) distinguishing early equine OA synovial fluid (SF) (8). These include microRNAs (miRNAs) and snoRNAs, which are functional RNA molecules that are transcribed from DNA but do not translate into proteins (9). It is expected that novel therapies can be directed in a personalised manner (10) following disease stratification. This is imperative as OA therapies are currently only symptomatic; principally pain relief in the horse. Extracellular vesicles (EVs) from purified cell types are suggested as novel therapeutics in a number of human diseases including rheumatoid arthritis (11), cardiac disease (12), and neoplasia (13). EVs have the potential to accelerate tissue regeneration and improve tissue functions. In disease treatment, they have been suggested as treatment strategies and delivery systems. Stem cell-derived EVs can recapitulate their parent cells, with advantages of being less immunogenic and capability of crossing biological barriers (14).
We have identified DE sncRNAs in ageing equine and human and OA human cartilage (15,16), equine OA SF (8), and mouse serum (17, 18). In human OA, others have identified sncRNAs in joint tissues, plasma, and SF as potential therapeutics (19,20) or possible OA biomarkers (18, [21][22][23][24]. EVs produced by cells transfer molecules (including sncRNAs) between cells and tissues (25) and are known to be found in serum, SF, articular cartilage, and supernatants of synoviocytes and chondrocytes (26). EV cargo is involved in cross-talk between cells in joint tissues and affects ECM turnover and inflammation (27,28), representing a crucial step in OA regulation. The role of EVs in OA provides a foundation to create novel disease-modifying treatments (27). Promising results were obtained in therapeutic application of mesenchymal stem cell-derived EVs for cartilage repair and experimental OA (29). Additionally, EVs have a therapeutic potential in human rheumatoid arthritis when administered either into the joint or systemically (27). Whilst EVs can carry various types of cargo, increasing evidence points at miRNAs as considerable mediators for the effects of these vesicles on their target cells (30, 31). Significantly, miRNAs regulate signalling pathways and the immune system related to extracellular matrix synthesis, chondrocyte survival, and proliferation (32, 33).
Animal models of OA enable the reproduction and progression of degenerative damage to be measured in a controlled manner, enabling opportunities to monitor and modulate symptoms and disease progression (34). Although the equine carpal osteochondral fragment model (35, 36) does not encompass all pathophysiological aspects of different OA phenotypes, it allows a controlled system with known time of onset and a singular cause of OA, facilitating temporal OA progression studies. Molecules can be measured temporally to determine their role in early OA. This is particularly imperative as the precise onset of molecular events of early OA following trauma is not completely understood. Furthermore, the size of the equine middle carpal joint permits repeated SF sampling over time, thus limiting use of experimental animals. Thus, the carpal fragment model enables a simultaneous study on equine OA with the ability to apply the findings to understand human OA.
We hypothesise that the EV sncRNA cargo from SF and plasma can be used to identify OA in an early stage before clinical signs and irreversible cartilage degradation. Additionally, by determining changes in sncRNAs in a longitudinal manner, we may be able to further understand the pathogenesis of early OA.

Horses and study design
The Danish Animal Experiments Inspectorate (permit 2017-15-0201-01314) and local ethical committee of the Large Animal Teaching Hospital of University of Copenhagen approved the experimental protocol. All procedures were undertaken according to EU Directive 2010/63/EU for animal experiments.
Four skeletally mature standard-bred trotters (2.5-7 years old, weighing 397-470 kg) were included in this study. Prior to inclusion, the horses underwent clinical examination, lameness examination including flexion tests, radiographic imaging, haematological and blood-biochemical analysis, and arthrocentesis of both middle carpal joints to ensure that the horses were sound with healthy joints.
OA was surgically induced in the left middle carpal joint, and the right middle carpal joint underwent sham surgery as described previously (36). Plasma and SF were sampled from both middle carpal joints before and following OA induction as described below.
The horses were euthanised on day 71/72 with pentobarbital sodium (140 mg/kg, Euthasol Vet, Dechra Veterinary Products, Uldum, Denmark). Following euthanasia, samples were collected from the joints as detailed below.

Induction of osteoarthritis and exercise
OA induction was undertaken as described by McIlwraith (36) under general anaesthesia. In brief, an osteochondral fragment was created in the third facet of the radial carpal bone SF and plasma sampling SF samples were obtained from both middle carpal joints prior to (day 0) and 10,35,42,49,56, and 63 days after the surgery. However, the samples of SF and plasma were taken at additional time points prior to day 10 and on day 70 these had been used in previous studies. Therefore, we used time points where we had sufficient volumes for our experiments. Following sedation with detomidine (0.01 mg/kg, Domosedan Vet; Orion Pharma Animal Health, Copenhagen, Denmark) and butorphanol tartrate (0.01 mg/kg, Dolorex; MSD Animal Health, Copenhagen, Denmark), SF was aspirated aseptically with a 19-gauge, 40-mm needle into ethylene diamine tetra acetic acid (EDTA) tubes (BD Vacutainer, BD A/S, Albertslund, Denmark). These were inverted 5-10 times, kept on melting ice, and centrifuged at 1,000 g for 20 min at 4 • C. Plasma was collected at the same time points following intravenous sampling from an indwelling catheter in the jugular vein on days 0-14 and thereafter by venipuncture. Samples were collected directly into EDTA tubes, kept on melting ice, and centrifuged at 3,000 g for 15 min at 4 • C. Biofluids were processed within 1 h and stored at −80 • C immediately after centrifugation.

Post-mortem examination
Following euthanasia, the middle carpal joints were opened, and the synovial membrane and articular cartilage were obtained from the intermediate carpal and third carpal bone and placed in neutral buffered 10% formalin. For histology, the samples were processed for haematoxylin and eosin and safranin O (cartilage only) staining. Histological grading of the synovial membrane and cartilage was performed (38).

EV isolation
The SF and plasma collected on days 0, 10, 35, 42, 49, 56, and 63 from OA and control joints were thawed, and SF was subsequently treated with 1 µg/ml hyaluronidase (from bovine testes; Sigma-Aldrich, Gillingham, United States). Both the SF and the plasma were centrifuged at 2,500 g for 10 min and 10,000 g for 10 min at 4 • C. EVs were subsequently isolated by size exclusion chromatography using qEV single columns (IZON, Lyon, France) following the manufacturer's instructions. Briefly, 3.5 ml of phosphate buffered saline (PBS; Sigma-Aldrich, Gillingham, United Kingdom), previously processed using a 0.22-µm polyethersulfone philtre (Sartorius, Göttingen, Germany) was passed through the column, followed by 150 µl of SF or plasma. The first five 200 µl that flowed through fractions were discarded, and the following five 200 µl fractions were pooled (isolated EVs).

EV characterisation Nanoparticle tracking analysis
For all the samples, 100 µl of isolated EVs were characterised by nanoparticle tracking analysis (NTA; Nanosight NS300; Malvern Panalytical Ltd., Malvern, United Kingdom) at 25 • C to determine particle size and EV concentration (39). Camera level, slider shutter, and slider gain were set at 12, 1,200, and 146, respectively. For each sample, three replicate 60-s videos were recorded and the results were averaged. For analysis, detection threshold was set at 5. Data analysis was performed with the NTA 3.2 software (Malvern Panalytical, Malvern, United Kingdom). A statistical analysis comparing the groups was conducted using mixed effects models with the Dunnett's and Sidak's multiple comparisons tests applied to plasma and SF-derived EVs, respectively, using GraphPad Prism v8.0.1. Significant differences between the groups were identified at p ≤ 0.05.

Exoview
SF and plasma EV isolates collected on days 0, 42, and 63 were concentrated using 2 ml Vivaspin concentrator columns (100,00 kDa cutoff) (Sartorius, Göttingen, Germany) by centrifuging at 1,000 g until the final volume was 100 µl and subsequently centrifuged into a column cap at 4,000 g for 2 min. Five µl of each sample was removed and pooled with all corresponding samples of the same group and time point. This single pooled sample from n = 4 horses was then analysed, with a total of six samples (day 0 control, day 0 OA, day 42 control, day 42 OA, day 63 control, and day 63 OA). ExoView analyses EVs using visible light interference in order to determine EV size, in addition to fluorescence to measure specific immunocaptured proteins. The samples were analysed in technical triplicate/sample using the human ExoViewTetraspanin Kit (NanoView Biosciences, United States). The samples were diluted in a manufacturersupplied incubation solution and incubated overnight at room temperature on ExoViewTetraspanin Chips. The chips were washed three times in solution A prior to incubation with fluorescent tetraspanin antibodies. Labelling antibodies consisted of anti-CD9 CF488, anti-CD81 CF555, and anti-CD63 CF647 and the MIgG negative control. The antibodies were .
/fvets. . diluted to 1:500 as per the manufacturer's instructions and incubated on the chips for 1 h at room temperature. The chips were then washed in kit-supplied buffers, dried, and imaged with ExoView R100 using ExoView Scanner v3.0. Data were analysed using ExoView Analyzer v3.0. Fluorescent cut-offs were set relative to the MIgG control. Total EVs were determined as the number of detected particles bound to tetraspanin antibodies (CD9, CD81, and CD63) and normalised to MIgG antibody. Particle diameter and counts were statistically analysed by repeated measures ANOVA in GraphPad Prism v9.0.1 and Excel. Significant differences between the groups were identified at p ≤ 0.05.
Small RNA sequencing RNA isolation, library preparation for small RNA-Seq, and sequencing Total RNA was extracted from 200 µl of isolated EVs using the miRNeasy Serum/Plasma Advanced Kit (Qiagen, Crawley, United Kingdom) and following the manufacturer's instructions, including supplementation of an RNA spike-in (Qiagen, Crawley, United Kingdom) and on-column DNase treatment (Qiagen, Crawley, United Kingdom). In addition, glycogen (Thermo Scientific, Waltham, Massachusetts, United States) was added to a final concentration of 50 pg/ml prior to adding isopropanol. Because of low RNA yield, which impairs accurate determination of RNA concentrations, a fixed total RNA volume of 2 µl was used for library preparation using a CleanTag kit (TriLink, San Diego, United States). Adapter dilution (1:4) and PCR amplification (26 cycles) were optimised in a pilot experiment using Bioanalyzer DNA1000 (Agilent, Santa Clara, United States) chips as a read out of library size and quantity. The samples were then processed in two batches of 37 samples. Library yield and size range were confirmed for all the samples using Bioanalyzer DNA1000 chips (Agilent, Santa Clara, United States). Equimolar amounts of all the samples were pooled, and size purification was performed using BluePippin (Sage Biosystems, Beverly, United States). To maintain larger RNA fragments, the size range was extended to 130-300 bp, which corresponds to insert sizes between 15 and 180 bp. The purified pool was sequenced on a NovaSeq SP100 flow cell (Illumina, San Diego, United States).

Small RNA sequencing data processing
Data were analysed using the miND pipeline (40). The overall quality of the next-generation sequencing data was evaluated automatically and manually with fastQC v0.11.9 (41) and multiQC v1. 10 (42). Reads from all passing samples were adapter-trimmed and quality-filtered using cutadapt v3.3 (43) and filtered for a minimum length of 17 nt. Mapping steps were performed with bowtie v1.3.0 (44) and miRDeep2 v2.0.1.2 (45), whereas the reads were mapped first against the genomic reference EquCab.3.0 provided by Ensembl (46), allowing for two mismatches and, subsequently, miRBase v22.1 (47), filtered for miRNAs of Equus caballus only, allowing for one mismatch. For a general RNA composition overview, non-miRNA mapped reads were mapped against RNAcentral (48) and then assigned to various RNA species of interest. A statistical analysis of pre-processed next-generation sequencing data was conducted with R v4.0 and the packages pheatmap, pcaMethods v1.82, and genefilter v1.72. A DE analysis with edgeR v3.32 (49) used the quasi-likelihood negative binomial generalised log-linear model functions provided by the package. The independent filtering method of DESeq2 (50) was adapted for use with edgeR to remove low abundant miRNAs and thus optimise false discovery rate (FDR) correction. Significantly, DE sncRNAs were determined at p < 0.05.

Bioinformatics of di erentially expressed miRNAs
Potential biological associations of the DEmiRNAs in EVs derived from plasma or SF were identified using Ingenuity Pathway Analysis (IPA; Qiagen, Redwood City, CA, United States) "Core Analysis." Furthermore, to identify putative miRNA targets, we utilised the MicroRNA Target Philtre module within IPA. We used a conservative filter within IPA of experimentally validated and highly conserved predicted mRNA targets for each miRNA. ToppGene was used for functional enrichment analysis of the miRNA targets (51) with a Bonferroni FDR < 0.05. Biological process gene ontology (GO) terms generated through ToppGene were then summarised, and REViGO (52) and Cytoscape (53) were used to visualise the network.

Model outcome
As previously described, the end point synovial membrane scores were significantly increased, with increased cellular infiltration, intimal hyperplasia, subintimal oedema, and increased total final histological score in the OA joints vs. control joints (p < 0.05). Additionally, a previous histological evaluation of the third carpal bone cartilage demonstrated a significant increase in chondrocyte necrosis, cluster formation, and focal cell loss scores in OA joints, and a significant increase in final score (p < 0.05) (54). Representative examples of gross photographs of a control carpal joint and a carpal joint following OA induction plus representative histological sections are shown in Supplementary material 1.

EV characterisation
Nanoparticle tracking analysis The mean and mode particle size for control SF were 219.8 and 180.9 nm, OA SF 221.8 and 195 nm, and plasma 158.4 and 123.3 nm. We did not identify differences for SF-derived EVs in size, size distribution, or concentration between the control and OA joints. However, for plasma-derived EVs, a reduction in the mean EV size was identified on day 49 compared to day 0, and a reduction in D90 was identified on day 63 compared to day 0. No changes in plasma EV concentrations were identified over time ( Figure 1 and Supplementary material 2).

Exoview
We analysed the vesicle count, size, and heterogeneity of SF and plasma EVs using Exoview in accordance with MISEV guidelines (55) in order to validate that the particles isolated were EVs. The human ExoView chip assessed CD9, CD63, and CD81 markers. For all the equine samples, there was minimal binding on CD63 capture, suggesting low-sequence homology between human and equine CD63. CD63 data were therefore excluded from further analysis. Particles stained for CD9 and CD81 were more obvious in plasma-derived EVs ( Figure 2D) and CD81-positive EVs ( Figure 2E) following the OA induction. Interestingly in the SF, there was no difference in vesicle diameter with time or OA for CD9 ( Figure 3B). However, there were differences in temporal and disease-related expressions of CD81-captured EVs in the SF ( Figure 3C). There were statistical differences in temporal expression of CD9 EVs in the control but not in the OA samples ( Figure 3D). For CD9 and CD81 in the control samples only, expression was lowest on day 42 ( Figures 3D,E). The expression of CD81-labelled vesicles was significantly different between OA and control on days 0 and 42 in the SF ( Figure 3E). From the co-localisation results of CD9 and CD81, there was an apparent drop-off in the proportion of EVs .

Small RNAseq results
In the SF and plasma-derived EVs, we identified multiple classes of non-coding RNAs including miRNAs, snoRNAs, small nuclear RNAs (snRNAs), transfer RNAs (tRNAs), long non-coding RNAs (lncRNAs), y-RNAs, piwi RNAs (piRNAs), and scaRNAs (scRNA). Supplementary material 4 shows the number of each class of miRNA, snoRNA, snRNA, tRNA, lncRNA that was identified in at least 30% of the samples in that group (plasma or synovial fluid-derived EVs).
miRNAs First, we analysed the changes in plasma-derived EV miRNAs before and after the OA induction by comparing day Frontiers in Veterinary Science frontiersin.org . /fvets. .   Figure 4A; Table 1) and found 16 different DE miRNAs.

Other non-coding RNAs
Pairwise comparisons for other non-coding RNAs considering the donor effect were conducted on the plasma and SF. In the plasma, we found five tRNAs, eight lncRNAs, and five snRNAs DE (FDR < 0.1) Additionally, in the SF, we identified five tRNAs, two lncRNAs, and four snRNAs DE (FDR < 0.1; Supplementary material 6).

Bioinformatics analysis of DE miRNAs in plasma or SF Plasma pairwise comparison
For the plasma-derived EVs, we first looked at the set of 16 DE miRNAs following pairwise comparisons compared to day 0. IPA identified the top diseases and functions with negative activated Z-scores (used to infer the activation states of biological functions based on comparison with a model that assigns random regulation directions; for negative, this is related to prediction of inhibition) as migration (p = 9.14 × 10 −4 ), cell proliferation (p = 5.32 × 10 −5 ), and cell viability (p = 8.7 × 10 −2 ; Figure 9A). To investigate the position of the 27 (22 when duplicates were removed) DE miRNA plasma expression networks, we then determined their putative target genes using IPA Target Philtre, which integrates computational algorithms with multiple miRNA databases. We used a threshold of "experimentally validated" or "highly predicted." This matched the miRNAs to 920 mRNA targets. The mRNA targets were input into the gene ontology tool ToppGene; then, 1,626 biological processes identified were visualised using Revigo and Cytoscape ( Figure 9B). The top 100 biological processes were visualised in Treemap ( Figure 9B).

Discussion
Fundamental studies that contribute to characterisation and understanding of early OA will serve to elucidate disease pathogenesis and identify novel molecules as biomarkers for early detection or that act as therapeutic targets. The demonstration that EVs are a method of cell communication has caused a recent increase in publications focused on EVs (56,57). In this study we have, for the first time, studied the sncRNA EV content temporally in OA using an equine model. In our previous study, we have confirmed the development of OA in our equine carpal osteochondral fragment model by histological evaluation. There was significant cartilage degradation and synovial membrane inflammation in OA joints (54).
Similarities in the pathogenesis and clinical signs of OA in humans and horses are leading the expansion of equine translational studies (58, 59), and with the advantages of histological and anatomical similarities to human joints, it is easy to conduct longitudinal sampling of SF and blood in the animal model. However, the use of a horse as a model is limited by high costs and ethical considerations and reduces the number of animals in studies. In our experiment, we had access to samples from four horses and were able to examine, for the first-time in any species, temporal OA changes in EV characteristics and Frontiers in Veterinary Science frontiersin.org . /fvets. .

FIGURE
(A-R) Di erentially expressed miRNAs isolated from plasma-derived extracellular vesicles following small RNA sequencing. Individual panels for miRNAs di erentially expressed using pairwise comparisons of all the six time points (days , , , , , and ), miR, eca-miR, n = horses. Error bars ± standard deviation. *p < . , **p < . , and ***p < . . sncRNA cargo using SF and plasma and spanning seven time points. Whilst our groups have examined the sncRNA cargo in equine SF in early OA (8) and the temporal changes in miRNAs in SF from the same equine osteochondral fragment model (36), here, using less time points, we tried to identify the changing sncRNA landscape in SF or plasma-derived EVs. It is challenging for most experimental designs to discriminate exosomes from microvesicles in terms of their size, cargo, properties, and origin (60). Consequently, most research represents the characteristics of a heterogenous group of nanovesicles, which are derived from both endosomal multivesicular bodies and plasma membrane budding. In our study, we used both NTA and interferometric images (Exoview) using tetraspanin antibodies to size and count individual EVs. First, we conducted NTA to characterise the EV profile in all the samples of SF and plasma from the model. We then used pooled the samples at selected time points for ExoView to financial constraints. To isolate our EVs, we used carried out exclusion chromatography to separate EVs from proteins and lipids as we had only small volumes of our biological fluids. Others have recommended this isolation method when there are limited volumes of plasma and SF, and downstream analysis includes sncRNA and protein cargo (58, 59). Ultracentrifugation alone of SF-derived EVs increased contamination and aggregation, whilst size exclusion chromatography increased EV enrichment (61).
EVs are involved in multiple functional roles in joint homeostasis [reviewed (62)]. The EV characteristics of size and concentration can be affected by any external stimulus, which significantly alters the formation, release, and uptake of EVs (63). By NTA, we measured overall sizes and .
/fvets. . quantity of isolatable EVs. This method enables acceptable repeatability and reproducibility (64). However, the results must be interpreted considering inherent biological sample diversity. The samples contained a heterogenous group of EVs of various sizes and are comparable only with a caution to preparations from cell culture and other plasma or SF studies as results are also dependent on methods of EV isolation.
Having utilised the size exclusion chromatography method, our measurements displayed minimal levels of background noise reflecting the reduction of protein aggregates or cellular components compared to ultracentrifugation isolation methods (59). Our NTA size estimates were within EV range and similar to other plasma (65) and SF (61, 66) studies using the size exclusion chromatography method. However, compared to a recent plasma EV study on human OA using EV precipitation and nanoparticle tracking methods, the equine plasma EVs were smaller (human 235 nm, equine 158 nm) (24), which may be because of the isolation methods used. Comparing both biofluids, the measured particle sizes were overall greater in the SF than in the plasma. We did not find any difference in EV concentration, size, mode, D10, D50, or D90 in the SF temporally or with OA induction in the model. However, mean plasma EV size was found to be decreased on day 49, and D90 was decreased on day 63. Interestingly there are few studies characterising the size and concentration of EVs in SF or plasma of normal and OA samples. Our results concur with one study in which NTA was performed to characterise EVs in normal human and OA SFs (66). However, it should be noted that a precipitation exosome kit was used in this study for EV isolation. Following exosome isolation from human knee SF, there was increase in exosome concentration from early to severe knee OA without changes in the average particle size (67). In a further study on early and late stage knee OA following isolation of exosomes by ultracentrifugation of biofluids, whilst for plasma there was no difference in concentration of exosomes, in SF the expression of exosomes in early OA and late-stage OA was higher than that in controls (68). As NTA does not specifically measure EVs but also coisolates contaminants, we also used Exoview TM to characterise SF and plasma-derived EVs at selected time points. This technology is used to conduct single particle interferometric reflectance imaging sensing together with antibody-based chip capture and fluorescence detection that enables single vesicle identification. Additionally, it has a lower size limit of detection than scattering based techniques (69). We had previously tested EVs isolated from equine SF and plasma on both mouse and human tetraspanin chips and found that the human chips captured EVs more efficiently (data not shown). Whilst classical EV markers are CD9, CD63, and CD81, some cells secrete EVs devoid of CD63 (70) and so a panel of antibodies is preferable to confirm EV isolation. There was minimal staining for CD63 indicating either poor antibody sequence homology or rareness of the EVs. Unfortunately, we could not cheque for sequence Frontiers in Veterinary Science frontiersin.org . /fvets. .

FIGURE (A-T)
Di erentially expressed miRNAs isolated from synovial fluid-derived extracellular vesicles following small RNA sequencing. Comparisons were made between control (C) and osteoarthritis (OA) and between OA time points (days , , , , , , and ), miR; eca-miR, n = horses. Individual panels for the DE miRNAs altered between OA time points or between control and OA. Error bars ± standard deviation. *p < . , **p < . , and ****p < . .
homology because of proprietary constraints; therefore, some cautions should be used in not over-interpreting the results from antibody characterisation as we do not know the actual cross reactivity. However, for CD9, CD81, and CD9, protein sequence homology is 92, 95, and 84%, respectively. As we suspected a lack of sequence homology in our preliminary study, we did not analyse the CD63 results further. Because of prohibitive costs, we only ran a subset of the SF and plasma samples on the Exoview platform. In the plasma, there was an apparent change in size following the OA induction. In the SF, no temporal or OA-related change was evident for CD9-positive EVs; however, for CD81-positive EVs, there was. CD81 expression changed  tetraspanins (73). Simultaneous immunostaining enabled dual immunofluorescent analysis, and therefore co-localisation of CD9 and CD81 was assessed. In the plasma on days 0 and 42, there were similar co-localisation profiles, but the proportion of double-positive EVs was reduced significantly on day 63. This could potentially indicate a switch in tetraspanin phenotype in OA. The biological implications of this switch are unknown. One limitation of our study, which should be noted with respect to Exoview data, is the limited number of samples analysed; therefore, further studies are necessary before hard conclusions can be drawn.
Since the identification of RNA in EVs (74), there has been a rise in the interest of using EV-RNAs as biomarkers (75). Others have demonstrated that the relative concentrations of miRNAs secreted in EVs differs from those found in the cell, or serum/plasma concentrations of miRNAs (76). Because of the role of EVs in cell communication, we hypothesised that sncRNA changes due to OA may be found in both SF and plasma EVs. SF-derived EVs are likely to come from a number of sources including cartilage, subchondral bone, synovium, and the circulation. We used a non-biassed approach, small RNA sequencing, to remove bias, and whilst most blood (21,23,(77)(78)(79) or SF studies (21, 22) on OA have looked at miRNAs alone, we investigated a number of noncoding RNAs in our samples. To our knowledge, this is only the second study to investigate EVs derived from plasma in OA (24) and the first to describe SF-derived EV sncRNAs in OA.
To reduce contamination with residual platelets, all the plasma samples were centrifuged within 30 min after blood collection at 3,000 × g 15 min as previously recommended (80). Whilst we cannot discriminate contamination by miRs included in platelets in our plasma samples, this methodological step should have reduced it to a minimum. Future work could investigate platelet-derived EV markers such as CD31, CD41, CD42a (81) in order to determine the level of potential platelet contamination, or use flow cytometry and antibodies to remove potential platelet-derived EVs. We used EDTA collection tubes. miR content is different among plasma EVs, serum, and plasma (78). Care should be taken when comparing results not only between plasma and serum in similar studies but also in plasma studies depending on the collection tube used. Serum and citrate plasma contain increased numbers of platelet-derived EVs compared to acid citrate dextrose and EDTA plasma samples in one study (82).
In the plasma, we found 16 different DE miRNAs between day 0 and other time points and 22 DE after all the pairwise comparisons were conducted. We hypothesise that changes in the plasma could, in the initial few weeks, be due to OA and sham joint changes but would during the later stages of the model be due to OA changes alone. Of the miRNAs DE some have been found to change in OA in other studies; has-miR-126, has-miR-29 (23), and has-miR-146a in four human studies (21,23,78,83). Interestingly, whilst one study on plasma found no DE miRNAs in plasma-derived EVs in OA, miR-146a was one of the most highly expressed miRNAs identified (24). In our study, we demonstrated that eca-miR-146a was significantly increased between days 42 and 63 and 56 and 63. Serum has-miR-146a-5p was significantly increased in women with knee OA compared with controls (74), and has-mir-146a has a vital role in maintaining cartilage homeostasis and increases in cartilage in early OA (84,85). Furthermore, in .
/fvets. . a mouse study, surgically induced OA treated with a miR-146a inhibitor significantly alleviated cartilage destruction by targeting Camk2d and Ppp3r2 (84). Thus, others have identified an increase in miR-146 in human serum in OA, whilst we identified that it was also increased in plasma-derived EVs following OA induction. This seems an across species effect of cartilage degradation in OA, which can then be evident in blood and blood-derived EVs. The increase in plasma EV miR146 could be derived from OA joints. Further studies are necessary to determine if plasma EV-derived miR-146 is an effect of cartilage destruction and could be targeted to treat OA.
In the SF-derived EVs, we identified 31 miRNAs that were DE between the control and OA or temporally in OA. Some have been previously identified as DE in other studies, including eca-let7c, eca-miR-10a, eca-miR-122, eca-miR-215, and eca-miR-23a, in SF derived from horses 28 days after the osteochondral model was commenced in the original study containing a larger cohort of nine horses (54).
Seven miRNAs were DE in the plasma and SF-derived EVs when the data were analysed between different time points; eca-miR-451, eca-miR-25, eca-miR-215, eca-miR-92a, eca-miRlet-7c, eca-miR-486-5p, and eca-miR-23a. Thus, these miRNAs represent the most promising biomarkers for further studies. An ideal biomarker for early equine OA would be sourced from blood rather than SF, but the fact that changes occur in both biofluids suggests that they are associated with OA changes in our model. Interestingly, eca-miR-215, eca-let7-c, and eca-miR-23a were also identified in our study in which we isolated sncRNAs from SF. They were altered on day 28 compared to day 0 in the same model and the same horses (54). Eca-miR-23a was also increased in equine early OA SF (8) and increased .
/fvets. . in late vs. early human OA SF (22). Has-mir-23a contributes to OA progression by directly targeting SMAD3 (86). Generally, our miRNA findings support our other study with this model in which we found that OA disease progression caused early changes in SF miRNA expression patterns on day 28 (24). We can only compare the plasma-derived EV results with one previous non-temporal study in OA in which no DE miRNAs were identified. This study used 23 OA patients and 23 controls and isolated EVs by precipitation followed by sequencing the samples on a sequencing platform similar to that used in this study (24). These discrepancies may be related to differences in species, sample type, disease stage or severity, methodology, gender differences, and other factors among studies.
EVs are vehicles for exchange of bioactive signalling molecules in cartilage and between joint tissues to promote joint homeostasis and arthritis pathogenesis. SncRNAs in condition-specific EVs may function as a unique set of noncoding RNAs. We know that miRNAs in particular act as regulatory molecules, but that other sncRNAs may also act as components of EVs contributing to OA pathogenesis by mediating cell-cell and tissue-tissue communication in osteoarthritic joints. Understanding the role of plasma-derived EVs in OA pathogenesis needs further studies, which we are currently conducting. In order to understand the potential role of the panel of miRNAs changing in our model, we conducted a pathway analysis of the DE miRNAs. First, we took DE plasma-derived miRNAs following pairwise comparisons compared to day 0. This was to define changes at a pathway level temporally compared to prior to OA induction (day 0). A "Core Analysis" of the miRNAs in IPA described a predicted inhibition of migration, cell proliferation, and cell viability. The carpal osteochondral fragment model specifically simulates a post-traumatic OA phenotype. Therefore, every event from carpal chipping to erosion of cartilage is considered part of the pathogenesis of the disease. This suggests that plasma-derived miRNA EV cargo changing in the model would inhibit cell migration, proliferation, and viability. However, the origin of these EVs cannot be confirmed but, if related to the model, would represent changes occurring in both the OA and sham joint processes including wound healing and repair, bone, cartilage, and synovial damage, and reparative processes of these tissues plus inflammation. In an OA mesenchymal stem, cells migrate and proliferate into the damaged cartilage area (87), and inflammatory cells can migrate to the joint (88). By reducing migration, proliferation and viability, the plasma miRNA EV cargo could potentially affect the interplay of various cells in the OA joint.
Next, we took the 22 DE miRNA plasma expression network, used IPA to predict their target genes, and then condensed the list of biological pathways affected by these mRNAs. This identified many terms related to cell response (including would healing and endogenous stimuli), regulation of cellular processes (including cell death, proliferation, apoptosis, migration, and metabolic processes), and cell signalling, secretion, and activation. As the EV-miRNA cargo was from the plasma, it is difficult to distinguish if these effects are due to OA changes alone or arthroscopy of both the OA and sham joints. Despite this, our findings potentially offer an insight into early changes in post-traumatic OA. However, by investigating the potential pathways of DE miRNAs in the SF (which we hypothesise would be more likely to be due to disease induction), we detailed the potential role of this cargo and its effects in early post traumatic OA in the joint. Interestingly, whilst one would expect the Argonaute RISC catalytic component to be an upstream regulator of miRNAs [as the Argonaute family of proteins has a role in RNA interference (89)], E2F Transcription Factors 1 and 3 were also predicted. This has a role in accelerating cell proliferation and promoting inflammation signalling (90), and E2F1 targets may be affected in OA pathogenesis (91).
Additionally, we analysed the expression changes of other sncRNAs. To our knowledge, for the first time in an OA model, we have identified changing plasma and SF-derived EV tRNAs, lncRNAs, and snRNAs. These could be important molecules for understanding and treating OA. For example, Liu et al.  GO terms. Hierarchical level is represented by a di erent coloured rectangle (branch), each containing smaller rectangles (leaves). The size of the space inside each rectangle is based on the measured value. GO biological processes associated with dysregulated miRNA targets were identified following the TargetScan philtre module in IPA. ToppGene was used to perform a functional enrichment analysis of predicted miRNA targets to highlight biological processes most significantly a ected by dysregulated miRNA-mRNA interactions. GO terms (FDR < . ) were summarised and visualised using Revigo and Cytoscape. The allowed similarity setting in Revigo is medium.
showed that exosomes derived from human mesenchymal stem cells contained lncRNA KLF3-AS1 and promoted chondrocyte proliferation in vitro, whilst in an in vivo collagenase-induced OA model, these exosomes improved cartilage repair (92).
We also investigated the changes in snoRNAs. From our previous study, we have known that snoRNAs have an important role in joint homeostasis (93), mouse joint ageing (17), chondrogenic differentiation (94), cartilage ageing (15), OA (6,7), and early OA SF (8). Interestingly, in the plasma, we found that 82% of DE snoRNAs in the plasma were also identified in ageing/OA human cartilage study (7)  in oxidative stress (97). For DE snoRNAs the in synovial fluid, 88% of DE snoRNAs in the plasma were also identified in an ageing/OA human cartilage study (7) including snord15, snord2, snord21, snord46, snord58, snord96A, and U3. Furthermore, snord96A was DE in equine SF in early OA (8) and snord58 in ageing human cartilage (unpublished work). Four snoRNAs, U3, snord15, snord46, and snord58, were found to be DE in both plasma and SF-derived EVs and were all altered in pairwise comparisons in OA. Changes in U3 are particularly interesting given our knowledge of its role in OA and impact on protein translational apparatus (6). This snoRNA was reduced in the plasma at multiple time points and in the SF between days 0 and 56. It is essential for rRNA maturation, acting as a spliceosome during ribosome biogenesis and releasing 18S rRNA from the precursor 47S rRNA (98). Impairment of 18S rRNA is suggested to further decrease levels of 5.8S and 28S rRNA in experimental U3 impaired human articular chondrocytes (6), whilst in OA human articular chondrocytes following U3 downregulation, there was a reduction in the expression of chondrogenic genes accompanied by increased levels of chondrocyte hypertrophy genes. OA-related U3 downregulation affects the protein translation capacity of chondrocytes in vitro (6). Additionally, sequences of U3 have been suggested to have miRNA-like abilities, accomplishing effective RNA-silencing in vitro in at least two human cell lines (98). Overall, the changes in snoRNAs in EVs in both the plasma and the SF provide an insight into their role in the pathogenesis of OA, as traditionally OA has been defined as an imbalance between joint anabolism and catabolism. With our increasing evidence that it is an acquired ribosomopathy (93), there is a potential role for EVderived snoRNAs to contribute to OA pathogenesis via its cell communication role. We are currently studying sncRNA crosstalk in joint cells to shed more light on this hypothesis (99). Thus, EV snoRNAs may provide novel molecular opportunities for the development of OA therapeutics. We realise that there are a number of limitations in the study. The plasma and SF samples used in this study were stored at −80 • C for 2 years prior to analysis, and we were limited in the volumes of plasma and SF we had because the samples were used for multiple platforms. Although collection, handling, and storage were the same for all the samples, any inconsistencies in these procedures may alter the levels of sncRNAs. We used spike-ins throughout the sequencing methodology to increase reproducibility, and indeed the number of sncRNAs was similar to those observed by others in plasma-derived EVs (24), suggesting that our results are likely to be representative for sncRNAs in SF and plasma EVs. The age range of the donors was 2.5-7 years old. Therefore, we cannot discount possible agerelated changes in the EV characteristics or sncRNA expression identified in this study. We have previously described that mouse .
/fvets. . serum miR expression in young (8 months) vs. old (18 months) mice changes (18). Our laboratory found no changes in SFderived EV characteristics or selected miR expression between young (0-5 years) and old (14-21 years) horses (100). Unfortunately, we were unable to validate our findings in the same samples by qRT-PCR as there was not enough remaining sample for EV extraction.

Conclusion
Sequencing of temporal samples for sncRNAs in an equine model produced unbiased profiling of the circulating and SF-derived EV sncRNAome and identified a unique panel of DE sncRNAs during initiation and progression of early post-traumatic equine OA. We characterised plasma and SFderived EVs in equine OA for the first time and demonstrated that differences in tetraspanin expression may indicate that in early OA they could represent changing functionalities of EVs. The seven DE miRNAs in plasma and SF-derived EVs, eca-miR-451, eca-miR-25, eca-miR-215, eca-miR-92a, eca-miRlet-7c, eca-miR-486-5p, and eca-miR-23a, and four snoRNAs, U3, snord15, snord46, and snord58, identified temporally between specific time points represent exciting molecules for future studies.

Data availability statement
The NGS data presented in this study are available using GEO ID GSE200330.

Ethics statement
The animal study was reviewed and approved by the


s2fieldsofstudy: Biology Medicine

text:
Metallothioneins in Dental Implant Treatment Failure and Periodontitis in Patients with Down’s Syndrome: Validation of Results

Metallothioneins (MTs) are low molecular weight cysteine-rich proteins that can bind up to seven zinc ions. Among their numerous functions, MTs appear to act as protectors against oxidative and inflammatory injury. In our first published study, we reported downregulation of the isoforms MT1B (fold distance (FD) −2. 95; p = 0.0024), MT1F (FD −1.72; p = 0.0276), MT1X (FD −3.09; p = 0.0021), MT1H (FD −2.39; p = 0.0018), MT1M (FD −2.37; p = 0.0092), MT1L (FD −2. 55; p = 0.0048), MT1E (FD −2.71; p = 0.0014), MT2A (FD −2.35; p = 0.0072), MT1G (FD −2.24; p = 0.0118), and MT1A (FD −2.82; p = 0.0023) by comparing Down’s syndrome patients with periodontal disease and implant failure to those without periodontal disease and with a positive progression of their implants. In this gene validation study, we intended to verify the results of our first gene expression analysis. Materials and Methods: In our retrospective case–control study, we performed retrotranscription (RT-qPCR) of 11 RNA-to-cDNA samples using the SuperScript™ VILO™ kit (50; reference 1,176,605) from Thermo Fisher. We conducted the study using the real-time PCR technique on the q-PCR ViiA 7 platform from Thermo Fisher. We chose the format of the Taqman Array Plate 16 Plus (reference 4,413,261) from Thermo Fisher, which accommodates 12 genes plus four controls (GAPDH, 18S, ACTB, and HPRT1). We conducted the analysis of the plates using the Thermo Fisher Cloud Web Software. Results: The results obtained through gene validation analysis show that in PD+RI+ patients, the genes encoding the isoforms MT1F (FD 0.3; p = 0.039), MT1X (FD 338; p = 0.0078), MT1E (FD 307; p = 0.0358), and MT2A (FD 252; p = 0.0428) continue to show downregulation, whereas MT1B (FD 2.75; p = 0.580), MT1H (FD 281; p = 0.152), MT1L (FD 354; p = 0.0965), and MT1G (FD 336; p = 0.0749) no longer show statistically significant results.

Several studies have confirmed that MTs function as cytoprotective agents against oxidative and inflammatory injury because they can scavenge a wide range of reactive oxygen species, and their ability to scavenge hydroxyl radicals is 3009 times more potent Genes 2022, 13, 1028 2 of 11 than that of glutathione, the most abundant antioxidant in the cytosol [1]. Furthermore, MTs appear to inhibit certain pro-inflammatory cytokines, such as the interleukins IL-6 and IL-12, as well as the tumor necrosis factor (TNF-α), and thus may suppress inflammation [3,5]. Similarly, in vitro studies show that exo-MT can induce potent lymphocyte proliferation [4,10] and even increase it in the presence of lipopolysaccharide [10].
In our first published study [11], we reported that Down's syndrome patients diagnosed with periodontal disease and implant failure showed alterations in the gene expression (downregulation) of MT1B, MT1F, MT1X, MT1H, MT1M, MT1L, MT1E, MT2A, and MT1G isoforms, confirming the hypothesis that the MT1G isoforms are not functional, and the alternative hypothesis that MTs are expressed differently when comparing Down's syndrome patients with periodontal disease and dental implant failure to those without periodontal disease and with a positive progression of their implants after two years.
Although details regarding all the functions MTs can perform remain unknown, based on the available literature, the alteration in the expression of MT1 and MT2 genes is related to alterations in the early stages of bone healing that can lead not only to the failure of osseointegration of dental implants but also to the presence of low-strength bone with elevated susceptibility to periodontal disease and peri-implantitis.
Through the present gene validation study, we wanted to verify the results we obtained from the first gene expression analysis [11].

Type of Study
This study was in accordance with the guidelines of the Declaration of Helsinki of the World Medical Association, Ethical Principles for Medical Research Involving Human Subjects [12]. Our retrospective case-control study approved by the corresponding Ethics Committee (Ethics Committee of the Hospital Virgen del Rocío; File PI-0081-2016).
The study was descriptive and observational, and the only invasive procedures performed on patients were the collection of a small amount of blood and a dental examination. The patient (or the person responsible for them) gave consent based on the research's direct benefits to the patient.

Selection of Patients and Study Groups
The study groups were composed of Down's syndrome patients diagnosed with periodontal disease (PD) at the time of the examination and failed implants (rejection of implants, RI) after two years of progression (PD+RI+) versus Down's syndrome patients without periodontal disease and with positive implant progression after two years (PD−RI−).
The study's exclusion criteria were patients without Down's syndrome, patients receiving treatment that could potentially affect bone metabolism, patients treated with short or immediately loaded implants, patients with active or untreated periodontal disease, and patients with implants that had a progression period of less than two years.
In our study, we do not reflect data concerning pocket depth measurements and/or attachment loss of the remaining teeth of the patients since these data are of no interest for our study, as our variable "periodontal disease (PD)" was taken as a dichotomous variable (yes (PD+)/no (PD−)), as well as implant failure (yes (RI+)/no (RI−)).
The diagnosis of periodontal disease, as well as the evolution of the implants, was made based on the patients' medical records and the comparison of panoramic radiographs (immediate postoperative period at two years progression) to calculate the marginal bone loss (MBL) and to differentiate whether the bone defects related to the implants were due to a placement defect or to bone loss resulting from peri-implantitis.
Calculate the MBL of bone loss using the Lagervall and Jansson index [13] validated for its use in this type of study by Corcuera-Flores et al. [14]. This method divides the implants into four groups according to their MBL:

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Grade 0: implants without marginal bone loss. • Grade 1: marginal bone loss of one-third or less of the total length of the implant. • Grade 2: one-third, but less than two-thirds, of the total length of the implant. • Grade 3: marginal bone loss greater than two-thirds of the total length of the implant. • A fifth group (Grade 4) was added of those patients whose implant was lost.
In our study, a failed implant (RI+) is understood as lost after two years of follow-up, or peri-implant bone loss of at least grade 2 on the Lagervall and Jansson scale [13].
We retrieved demographic and clinical data from the patients' medical records and verified all essential data.

Sample Collection and Total RNA Isolation
During the patients' clinical examinations, two blood samples per subject were collected in PAXgene®tubes (reference 762,165) for RNA extraction, and one more blood sample was collected in a 3-mL VACUTTE tube with EDTA (anticoagulant) for DNA extraction.
The samples were transported to the processing center refrigerated at 2-8 • C for up to 5 days. The tubes were stored at −20 • C or −80 • C as appropriate.
RNA samples were extracted using the PAXgene™ BLOOD miRNA KIT (reference 763,134) from QIAGEN. The extraction was carried out in the QIAcube automated station of the same brand.
Subsequently, we prepared a database of the samples, detailing, among other things, the RNA quantification data. First, we quantified RNA concentrations using a visible light spectrophotometer with the Thermo Nanodrop 2000C equipment to ensure that the samples were well processed before conservation. Second, we took a much more precise measurement-in this case, only using the samples selected for the first gene expression study-using fluorescence and the Thermo Qubit 3.0 equipment. (reference Q33,216; Singapore, Malaysia) from Invitrogen™ by Life Technologies (Thermo Fisher Scientific, Waltham, MA, USA).
We used the SuperScript™ VILO™ kit and not the High-Capacity RNA-to-cDNA kit because of its robustness; of the two, the VILO™ kit has a much higher tolerance to inhibitors, although the kits are quite similar in terms of efficiency. In this case, working from PAXgene tubes, we chose VILO in case any residues from the tubes were present in the samples and could generate some inhibition.
Normalization prior to RT is not mandatory, but it is recommended because even if 100% efficiency is assumed for cDNA conversion, including small amounts of RNA, it could lead to small variations in efficiency. The SuperScript™ VILO™ kit contains 4 L of the master mix, which already includes the enzyme. Thus, completing up to 20 L of final reaction with RNA (up to a maximum of 2.5 g) and water (if necessary) allows up to 16 L of RNA to be included. However, the endogenous RNA will correct a posteriori, so, as mentioned at the beginning, normalization is not obligatory, although it is always recommended.
In our case, we normalized the samples to 18 ng/L, which is the minimum amount. By placing 16 L in each RT, we place a total of 288 ng (0.288 g) of RNA in each RT (Table 1). After this normalization, we quantified the cDNA to check the efficiency, so no further normalization was necessary. We quantified the 11 samples' cDNA using a Nanodrop 2000C spectrophotometer.
The samples were kept in deep freeze at −20 • C until the end of the study. Subsequently, we stored the samples at −80 • C in case a re-extraction was needed.
We designed a table with the IDs of the assays corresponding to the gene validation study following the below steps: • Avoid genes located in the 5 UTR region because this is a variable region that can differ between different transcripts, which can cause analysis problems.

•
Select the best coverage that detects the highest possible number of transcripts that are usually located between 2 exons, where their amplicon is usually as small as possible.

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Selection of probe spans exon assay, to avoid detection of and contamination by genomic DNA (gDNA). We analyzed only complementary DNA (cDNA). • Selection of the smallest amplicon to ensure the best efficiency in cases of small degradation (i.e., if there are cuts in the DNA strand, the smaller the amplicon, the less likely it is that the cut is located in that region). • Include a negative retrotranscription (RT) control (i.e., include a sample that undergoes RT but where the RT enzyme is not included in the reaction). Thus, if amplification is finally obtained in that sample, it is because gDNA is being detected.
We performed the study using the real-time PCR technique on the q-PCR ViiA 7 platform from Thermo Fisher. Following the above indications, the format chosen was the Taqman ® Array Plate 16 Plus (reference 4,413,261; CA, USA) from Applied Biosystems™ by Thermo Fisher Scientific, which can accommodate 12 genes and 4 controls ( Table 2). Two samples can be loaded on each plate in technical triplicates, requiring a total of 6 cards to study 11 samples.
The endogenous genes to be studied were GAPDH (glyceraldehyde 3-phosphate dehydrogenase), 18S (18S ribosomal RNA), ACTB (B-Actin), and HPRT1 (hypoxanthine phosphoribosyltransferase 1). Three of these are enough for the Thermo Fisher Cloud Web Software from Applied Biosystems™ by Thermo Fisher Scientific (version 2021.1.1-Q1-21-build11, CA, USA) to decide which is the best.
We conducted the analysis of the plates using the Thermo Fisher Cloud Web Software.

Statistical Analysis
Statistical analysis was performed using the t-test of independent samples (one-tailed distribution, unequal standard deviation, p-value < 0.05). For quantitative real-time RT-PCR, the median values of the quantification cycle (Cq) for the target genes (MT1A, MT1B, MT1E, MT1F, MT1G, MT1H, MT1M, MT1X, MT2A, MT3 y MT4) and the reference genes (HPRT1, 18S, GAPDH, ACTB) were compared in both groups (PD+RI+ vs. PD−RI−). Table 2. Design of each plate with the genes to be studied and the controls. We worked with GAPDH, 18S, and HPRT1 because these are the endogenous genes that have been used in similar patients in other publications. Because there were two gaps left on the plate, we decided to introduce the endogenous B-Actin and repeat the MT1F to ensure that it was not downregulated.

Participants and Characteristics
We compared a total of 11 Spanish patients with Down's syndrome; seven of them were not diagnosed with periodontal disease and had positive implant progression at 2 years, whereas four patients had active periodontal disease together with implant failure (Table 3). Table 3. Clinical data of the patients included in the study. To increase the number of patients in the control group, we included Patients 9, 10, and 11, who did not yet have implants in place. However, these patients did not have periodontal disease and would therefore not be candidates for early failure after implant placement. Demographic data are not significant and do not influence the results obtained. No statistically significant differences were observed when comparing by sex, age, or any other factor other than the one taken as a reference, which is the presence or absence of periodontal disease and implant rejection or positive evolution of implants.
Due to the strict inclusion and exclusion criteria, very few patients were included in the study. In fact, the main reason for conducting a second study, gene validation analysis, based on the results obtained in our previously published study, was the small sample size with which we had worked.
Additionally, we would like to point out that we wanted to include a third study group formed by Down's syndrome patients not diagnosed with periodontal disease but who had failed implants; however, no patients with these characteristics were found, and this fact was also considered a result.

Gene Expression Analysis
The results were obtained from RNA analysis using the Affymetrix Microarray Software. We compared the blood samples from the patients with periodontal disease and negative implant evolution (PD+RI+) to those from the patients without periodontal disease However, MT3 and MT4 isoforms did not show significantly altered expression.

Gene Validation Analysis
The results obtained through gene validation analysis, using the t-test, show that in PD+RI+ patients, the genes encoding the isoforms MT1F (     In contrast, the gene validation analysis does not show statistically significant results when performed on the PD−RI− or PD+RI− sample groups (Figure 2).

Discussion
The present validation study confirmed that the genes for the MT1F, MT1X, MT1M, MT1E, and MT2A isoforms exhibit a statistically significant alteration of expression in Down's syndrome patients with active periodontal disease and implant failure.
We recall that the research's aim was to find out whether there are statistically significant differences in gene expression that could explain the different clinical situations at the oral level with respect to the progression of dental implants in patients suffering from the same systemic condition, chromosome 21 trisomy.
The results of altered MT expression that were first published came from the comparison between Down's syndrome patients with periodontal disease and implant failure (PD+RI+) after two years of progression versus Down's syndrome patients without periodontal disease and with a positive progression of their implants (PD−RI−). However, when we compared Down's syndrome patients with periodontal disease and implant failure (PD+RI+) to those with periodontal disease and a positive progression of their implants (PD+RI−), we observed no significantly statistical alterations in MT expression. This leads us to believe that when matching the periodontal disease variable, it is other genes and not the MT genes that are responsible for the variability in clinical presentation in terms of periodontal disease and dental implant failure, as published in one of our previous studies [15]. This hypothesis is supported by the fact that if the patient group is modified again to compare Down's syndrome patients with periodontal disease and positive implant progression (PD+RI−) to those without periodontal disease and with positive implant pr (PD−RI−), the MTs appear again with altered gene expression, downregulated for the PD+RI− group (Table 5). It should also be noted that no patients were found who did not have periodontal disease but had implant failure, which we also consider to be an important result. This could be explained by the hypothetical etiopathogenesis shared by both diseases. As is well known, periodontitis is defined as a multifactorial chronic inflammatory disease induced by periodontal pathogens whose sustained inflammation over time leads to the loss of tooth attachment through damage to the periodontium itself, loss of alveolar bone, and, ultimately, tooth loss. Periodontal disease has been linked to multiple systemic inflammatory diseases that share the same mechanisms at the molecular level (increased levels of pro-inflammatory cytokines) that would result in "systemic inflammation" [16][17][18][19][20]. These locally and systemically increasing inflammatory mediators, together with the virulence factors of periodontal pathogens, can lead to rapid peri-implant bone loss leading to dental implant loss. Therefore, if the patient does not have periodontal disease and, therefore, is assumed that his systemic inflammatory condition is within the limits considered nonpathogenic, a positive evolution of his implants can be expected, or in other words, failure of the dental implants due to a genetic inflammatory condition is not observed since the control of systemic inflammatory disorders leads to an improvement at the periodontal and peri-implant level showing antiinflammatory and antioxidant activities on the one hand, and stimulating properties that promote endothelial function, angiogenesis, and bone formation on the other [16].
Whereas the results for MT1F, MT1X, MT1E, and MT2A correlate with those obtained in the first instance in the previously published gene expression study [11], showing downregulated gene expression, MT1M shows a statistically significant altered expression but with a surprising result: in the validation, it was shown to be overexpressed, whereas in the gene expression study [11], it was observed to be downregulated.
Similarly, another study [22], more specifically, related MT1 and MT2 isoforms to an important role during the early stages of osteoblastic cell differentiation from mesenchymal stromal stem cells.
In contrast, as a source of zinc and copper, MTs are activators of several metalloenzymes, including alkaline phosphatase [5], an enzyme that appears to participate in the regulation of the proliferation, migration, and differentiation of osteoblastic cells, in addition to facilitating osteoid mineralization.
As discussed above, strict inclusion and exclusion criteria severely limited patient participation in this study. The small sample size is the main reason for the present gene validation study. Therefore, since our results in our first gene expression study [11] were positive and we decided to validate them, and since we have worked with good relevance criteria, and statistical significance levels below 0.05 (p-value) have again obtained satisfactory results, we do not consider it appropriate to increase the sample size. This is something we would have considered if the validation results had been negative.
On the other hand, our validated results may serve to guide new avenues of research into the failure of dental implants in Down's syndrome patients, and, in the not too distant future, the predictability of dental implant treatment prior to implant surgery may be known.

Conclusions
Although it is true that there remains much to study and understand about MTs and their role, and although we are only hypothesizing now, it is clear from this gene validation study that this difference in the degree of gene expression of at least the MT1F, MT1X, MT1E, and MT2A isoforms could partly explain not necessarily the failure of implants in this type of patient, but rather the complex gene network behind the implant failure process. This study also clarified that this gene is indeed involved in different metabolic pathways leading to the clinical situation PD+RI+, given that we have obtained satisfactory and confirmatory results despite the small cohort of patients with whom we worked.
However, much remains to be known about the etiopathogenesis of peri-implantitis and its relationship to periodontal disease. Although there are currently numerous studies that relate periodontitis to systemic disorders and their bidirectional improvements, in relation to the inflammatory state of the patient, when treating periodontal disease and/or a systemic condition, in certain cases, improvements in peri-implantitis are not observed.
The current lack of studies on peri-implantitis in patients with Down's syndrome highlights the need for further research on the subject that could facilitate the path of dental rehabilitation in this type of patient, as in certain circumstances it is the only available therapeutic option. Our validation study aims to clarify the complex gene network behind implant failure in these patients in order to be able to direct dental treatment and predict the response to periodontal and implant therapy in the future.  Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.

Conflicts of Interest:
The authors declare no conflict of interest.


extfieldsofstudy: Biology Medicine

text:
Activation of targetable inflammatory immune signaling is seen in myelodysplastic syndromes with SF3B1 mutations

Background: Mutations in the SF3B1 splicing factor are commonly seen in myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML), yet the specific oncogenic pathways activated by mis-splicing have not been fully elucidated. Inflammatory immune pathways have been shown to play roles in the pathogenesis of MDS, though the exact mechanisms of their activation in splicing mutant cases are not well understood. Methods: RNA-seq data from SF3B1 mutant samples was analyzed and functional roles of interleukin-1 receptor-associated kinase 4 (IRAK4) isoforms were determined. Efficacy of IRAK4 inhibition was evaluated in preclinical models of MDS/AML. Results: RNA-seq splicing analysis of SF3B1 mutant MDS samples revealed retention of full-length exon 6 of IRAK4, a critical downstream mediator that links the Myddosome to inflammatory NF-kB activation. Exon 6 retention leads to a longer isoform, encoding a protein (IRAK4-long) that contains the entire death domain and kinase domain, leading to maximal activation of NF-kB. Cells with wild-type SF3B1 contain smaller IRAK4 isoforms that are targeted for proteasomal degradation. Expression of IRAK4-long in SF3B1 mutant cells induces TRAF6 activation leading to K63-linked ubiquitination of CDK2, associated with a block in hematopoietic differentiation. Inhibition of IRAK4 with CA-4948, leads to reduction in NF-kB activation, inflammatory cytokine production, enhanced myeloid differentiation in vitro and reduced leukemic growth in xenograft models. Conclusions: SF3B1 mutation leads to expression of a therapeutically targetable, longer, oncogenic IRAK4 isoform in AML/MDS models. Funding: This work was supported by Cincinnati Children’s Hospital Research Foundation, Leukemia Lymphoma Society, and National Institute of Health (R35HL135787, RO1HL111103, RO1DK102759, RO1HL114582), Gabrielle’s Angel Foundation for Cancer Research, and Edward P. Evans Foundation grants to DTS. AV is supported by Edward P. Evans Foundation, National Institute of Health (R01HL150832, R01HL139487, R01CA275007), Leukemia and Lymphoma Society, Curis and a gift from the Jane and Myles P. Dempsey family. AP and JB are supported by Blood Cancer UK (grants 13042 and 19004). GC is supported by a training grant from NYSTEM. We acknowledge support of this research from The Einstein Training Program in Stem Cell Research from the Empire State Stem Cell Fund through New York State Department of Health Contract C34874GG. MS is supported by a National Institute of Health Research Training and Career Development Grant (F31HL132420).

Introduction
Myeloid malignancies myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) are associated with dismal prognosis and need newer therapeutic options. Mutations in spliceosome genes are commonly seen in MDS and AML and SF3B1 is the most frequently mutated gene in patients with MDS (Yoshida et al., 2011). Even though reports have shown that mutations in SF3B1 can lead to widespread changes in splicing (Inoue and Abdel-Wahab, 2016), the exact pathways that are disrupted and lead to the pathogenesis of ineffective hematopoiesis and carcinogenesis in MDS and AML are not yet fully elucidated.
The innate immune signaling pathways have been shown to be activated in MDS and AML . The upstream activation of toll like receptors (TLRs), interleukin receptor 1 receptor accessory protein (IL1RAP), and interleukin 8 (IL8) activate the IRAK/TRAF6 pathways and lead to NF-kB activation (Mitchell et al., 2018). Even though these pathways have been shown to be overactivated, a link between this activation and commonly seen SF3B1 mutation has not been established. We had recently shown that another splicing mutation in U2AF1 splicing factor can lead to retention of exon 4 leading to overexpression of IRAK4 in MDS and AML (Smith et al., 2019). In the present report, we now demonstrate that SF3B1 mutation leads to exon 6 retention leading to overexpression of active isoform of IRAK4 known as IRAK4-long (IRAK4-L). We demonstrate that this IRAK4-L isoform containing the death domain can associate with MYD88 and lead to activation of downstream NF-kB signaling. These data demonstrate the link between SF3B1 mutation and oncogenic IRAK4 signaling in MDS/AML and demonstrate the therapeutic potential of inhibiting this pathway via pharmacological inhibitors.

Cell lines and human samples
HEK-293T and THP1 were purchased from the American Type Culture Collection. MDS-L was provided by Dr. Kaoru Tohyama (Kawasaki Medical School, Okayama, Japan) (Matsuoka et al., 2010;Rhyasen et al., 2014). Cell lines were cultured according to manufacturer's instruction in either RPMI 1640, IMDM, or DMEM medium supplemented with 10% FBS. MDS-L cell line was cultured in the presence of 10 ng/mL human recombinant IL-3 (Stem Cell Technologies). Cell lines were tested for mycoplasma and authenticated by STR. The MDS/AML patient samples used in this study were obtained with written informed consent under approval by the IRBs of the Albert Einstein College of Medicine. K562 isogenic cells with wild type (WT) and K700E mutation of SF3B1 were purchased from Horizon Discovery.

RNA sequencing of MDS CD34+ cells
Total RNA from MDS and control bone marrow CD34+ cells was extracted using TRIzol with a linear acrylamide carrier, treated with DNase I (Life Technologies) and purified using Agencourt RNAClean XP beads (Beckman Coulter). RNA quality was determined using an RNA 6000 Bioanalyzer pico kit (Agilent). A cDNA library was produced using a SMARTer library preparation protocol (Clontech). Sequencing (100 bp paired-end reads) was performed on an Illumina HiSeq4000. The reads were mapped to human genome GRCh37 using HISAT2 version 2.0.0-beta. Uniquely mapped read pairs eLife digest Genes contain blocks of code that tell cells how to make each part of a protein.
Between these blocks are sections of linking DNA, which cells remove when they are preparing to use their genes. Scientists call this process 'splicing'. Cells can splice some genes in more than one way, allowing them to make different proteins from the same genetic code.
Mutations that affect the splicing process can change the way cells make their proteins, leading to disease. For example, the myelodysplastic syndromes are a group of blood cancers often caused by mutations in splicing proteins, such as SF3B1. The disorder stops blood cells from maturing and causes abnormal inflammation. So far, the link between splicing, blood cell immaturity, inflammation and cancer is not clear.
To find out more, Choudhary, Pellagatti et al. looked at the spliced genetic code from people with myelodysplastic syndromes. Mutations in the splicing protein SF3B1 changed the way cells spliced an important signalling molecule known as IRAK4. Affected cells cut out less genetic code and made a longer version of this signalling protein, named IRAK4-Long. This altered protein activated inflammation and stopped blood cells from maturing. Blocking IRAK4-Long reversed the effects. It also reduced tumour formation in mice carrying affected human cells.
The molecule used to block IRAK4, CA-4948 -also known as Emavusertib -is currently being evaluated in clinical trials for myelodysplastic syndromes and other types of blood cancer. The work of Choudhary, Pellagatti et al. could help scientists to design genetic tests to predict which patients might benefit from this treatment.
were counted using htseq-count. The data have been deposited in the NCBI's Gene Expression Omnibus (GEO) repository (accession number: GSE114922).

Immunoblotting and immunoprecipitation
Total protein lysates were obtained from cells by lysing the samples in 1% NP-40 lysis buffer 20 mmol/l Tris-HCl, pH 7.5; 1 mmol/l EDTA; 150 mmol/l NaCl; (1% NP-40), containing phosphatase inhibitor cocktails 2 and 3 and protease inhibitors (Sigma-Aldrich), for 30-45 min at 4°C. Equal amount of protein was prepared by calculating protein concentration using Bradford reagent (Bio-Rad) and 50 μg of protein was resolved on 10-12% SDS-PAGE followed by transferring to nitrocellulose or PVDF membranes (EMD-Millipore). For immunoprecipitation, cells were lysed in 2% CHAPS lysis buffer (20 mmol/l Tris-HCl, pH 7.5; 150 mmol/l NaCl; 1 mmol/l EDTA; 2% CHAPS; Sigma-Aldrich) with protease and phosphatase inhibitors (Sigma-Aldrich) for 1 hr on ice. Protein lysates were incubated with primary antibody overnight at 4°C. Protein A agarose beads (Rockland Immunochemicals, Gilbertsville, PA) were added equally to all the samples and incubated for 1 hr 4°C. The beads were washed three times with CHAPS, eluted with loading buffer supplemented with 2-mercaptoethanol (Sigma-Aldrich) and western blotting was performed. In ubiquitination experiments, MG132 and N-ethylmaleimide (Sigma-Aldrich) were added to lysis buffer with dithiothreitol (Sigma-Aldrich) to prepare lysates. Western blot analysis was performed with the following antibodies: IRAK4 C-term (ab5985; Abcam), IRAK N-term

Clonogenic progenitor assays
For clonogenic assays with primary MDS/AML cells, primary patient samples and healthy controls (HC) were plated in methylcellulose (Stem Cell Technologies H4435, Vancouver, CA) with the CA-4948 and control, and colonies were counted after 14-17 days.

Xenografts
This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (001371) of the Albert Einstein College of Medicine. Mice were sacrificed and assessed for tumor burden measurements. For primary patient-derived xenografts NOD/SCID IL2Rgamma KO NSG mice were irradiated (200 rads) 24 hr prior to injection. Mononuclear cells from primary MDS patients were isolated by Ficoll separation. 2-5 × 10 6 MNCs were administered via tail vein injection. 3-4 weeks later, BM aspiration were performed and analyzed by flow cytometry for the human cell engraftment. Mice were considered to be engrafted if they showed 0.1% or higher human derived CD45+ cells. The engrafted mice were randomized for treatment with 12.5 mg/kg/d of CA-4948 or control 5 times a week for indicated times. Following treatment, BM aspirations and flow cytometry analysis for the above-mentioned markers were performed. All mice were bred, housed, and handled in the animal facility of Albert Einstein College of Medicine.

Reporter cell lines
IRAK4 exon 6 and its flanking intronic sequences were inserted into the EcoRI and BamHI sites of pFlareA vector. pFlareA-exon4 vectors were linearized by DraIII and transfected into 293T cells. Cell colonies stably expressing the reporter were selected by 1 mg/mL G418 and stable expression of GFP and RFP.

Polymerase chain reaction
Primer3 designed primers: Fw -TGAA CGAC CCAT TTCT GTTG G Rev -GAGT CTGT CTAG CAAT GAAC CA Expected product sizes: Exon 6 short isoform -194 bp Exon 6 long isoform -280 bp cDNA was generated using a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher). The primers listed above were used with Maxima Hotstart PCR mastermix to amplify the region containing exon 6 of IRAK4 and PCR products separated by electrophoresis on a 2% agarose gel.

Peptide docking
3D structure of CDK2 peptide was created using BioLuminate (Schrodinger LLC, New York). The peptide was docked to TRAF6 substate binding domain (PDB code: 1LB5) using Glide (Schrodinger LLC) using 'SP Peptide' mode that ensures enhanced conformational sampling of flexible polypeptides (Tubert-Brohman et al., 2013). To assure more accurate pose ranking, docked peptide poses were scored using implicit solvent MM-GBSA calculations. Final MM-GBSA interaction energies were used to create and rank peptide pose clusters.

SF3B1 mutations are associated with inclusion of exon 6 in MDS
We sought to determine whether primary MDS samples with SF3B1 mutations have altered splicing of transcripts involved in immune and inflammatory pathways. Analysis of RNA-seq of purified CD34+ HSPCs from HC and SF3B1 mutant MDS samples demonstrated an increased retention of full length exon 6 of IRAK4 in the SF3B1 mutant samples ( Figure 1A). The full length retention of exon 6 was confirmed by RT-PCR in a larger cohort of primary samples demonstrating a significantly greater ratio of long isoform of IRAK4 in SF3B1 mutant samples when compared to HC and MDS patients without splicing mutations ( Figure 1B and C). To gain further insight into the regulation of IRAK4 exon 6 inclusion in the presence of SF3B1 mutations, we generated an IRAK4 exon 6 cassette splicing reporter in HEK293 cells (HEK293-IRAK4exon6). Transfection of mutant SF3B1-K700E into HEK293-IRAK4exon6 cells resulted in inclusion of IRAK4 exon 6 from the splicing reporter compared to SF3B1-WT or vector-transfected HEK293-IRAK4exon6 cells ( Figure 1D), indicating that SF3B1-K700E directly mediates inclusion of IRAK4 exon 6. SF3B1 mutation leads to longer, stable, and functionally active isoform of IRAK4 To evaluate the effects of retention of full length exon 6 seen in SF3B1 mutant samples, we modeled the exon usage in vitro. Full length IRAK4 protein (IRAK4-L) is composed of a death domain, hinge region, and a kinase domain (Figure 2A). Exon 6 encodes a part of the kinase domain of IRAK4 protein. The exclusion of mRNA sequence encoding amino acids 188-217 in exon 6 as seen in HC can lead to two possible smaller IRAK4 isoforms. Isoform S1 (191 amino acids) could be produced due to formation of a premature stop codon (TGA) leading to a smaller IRAK4 isoform containing  death, hinge, and part of kinase domain. Isoform S2 (243 amino acids) (S2) could also be generated by utilizing an alternative translational start site (ATG) resulting in an IRAK4 isoform with only the kinase domain ( Figure 2A, Figure 2-figure supplement 1). We generated flag tagged plasmids of IRAK4 with full length exon 6 (IRAK4-L), and shorter S1 and S2 isoforms and transfected them in HEK-293T cells and evaluated their effects on NF-kB pathway activation. Immunoblotting analysis showed that plasmid with the full length exon 6 led to stable expression of IRAK4long protein and resulted into increased phosphorylation of p65 when compared to the smaller isoforms ( Figure 2B). By contrast, transfection of IRAK4 S1 and S2 constructs led to generation of lower molecular weight IRAK4 proteins with decreased expression when compared to the longer isoform ( Figure 2B). Treatment with proteasome inhibitor (MG132) led to increased expression of the smaller IRAK4 bands demonstrating that these were targeted for proteasomal degradation ( Figure 2C). Immunoprecipitation in HEK293T cells transfected with IRAK4-L, S1, S2, and HA-tagged Lys48 linked in presence of proteasome inhibitor MG132 demonstrated an increased association of Lys48 linked ubiquitin chains with shorter IRAK4 isoforms, validating ubiquitin mediated degradation ( Figure 2D).
Next, we wanted to determine whether expression of the SF3B1 mutation leads to changes in isoforms and downstream NF-kB activation. Isogenic cells with WT and K700E mutation of SF3B1 were evaluated and the mutant cells demonstrated overexpression of IRAK4-L and higher activation of p-p65 ( Figure 2E). Finally, we showed that the IRAK4-long containing the death domain was able to associate with MYD88 in immunoprecipitation experiments, while the shorter isoforms did not; thus, providing the mechanistic basis of activation seen only with the IRAK4-long isoform ( Figure 2F).

CDK2 is ubiquitinated at K63 residues downstream of IRAK4/TRAF6 in SF3B1 mutants
Having demonstrated overexpression of the active IRAK4-long isoform in SF3B1 mutant samples, we next wanted to determine the downstream pathways that are activated by these changes. Signal transduction via TLRs is primarily mediated by IRAK4-TRAF6 activation and TRAF6 is an E3 ligase that conjugates ubiquitin chains to itself and its substrates. The consensus sequence of TRAF6 binding motif is P(P-2)XE(P0)XX(Acidic/Aromatic)(P+3) and is present in known TRAF6 substrates ( Figure 3A). Since MDS/AML are characterized by ineffective hematopoiesis due to block in myeloid differentiation, we performed a search for the TRAF6 motif in cell cycle regulators and determined that CDK2 could be a potential substrate ( Figure 3A). Structural modeling of TRAF6 and CDK2 peptide show that positions P -2 , P 0 , and P 3 in CDK2 correspond to three distinct sub-pockets in the binding site of TRAF6 and supports the notion that CDK2 can be ubiquitinated by TRAF6 ( Figure 3B and Figure 3-figure supplement 1). To directly test the role of TRAF6 dependent ubiquitination of CDK2 in chronic innate immune signaling, we ectopically expressed CDK2, IRAK4, and TRAF6 in presence of either Lys48 linked ubiquitin or Lys63 linked ubiquitin. CDK2 was associated with a much stronger K63 ubiquitin that was dependent on TRAF6 and IRAK4 overexpression ( Figure 3C). To validate this result, we generated alanine and glycine mutants (CDK2 Myc 122 and CDK2 Myc 123) within CDK2 binding site of TRAF6 and examined their ubiquitination in presence of IRAK4-L and TRAF6 by immunoprecipitating CDK2. Immunoblotting analysis show that TRAF6 mediated Lys63 linked ubiquitination was decreased in CDK2 mutants as compared to wild type CDK2 ( Figure 3D).
IRAK4 exon 6 inclusion calculated as the ratio of the long isoform versus the short isoform from panel B. Data represent the mean ± SEM. P-values were obtained using Kruskal-Wallis test with Dunn's multiple comparisons test. * p<0.05, *** p<0.001 (D) Schematic of IRAK4 exon 6 splicing reporter. IRAK4 exon 6 and 50 bp of flanking introns were cloned into a splicing reporter (pFlare5A)55 and stably expressed into HEK293 cells. SF3B1 and SF3B1-K700E were transfected and IRAK4 exon 6 splicing was measured by RT-PCR using primers within pFlare5A. WT: wild type.   Figure 3E). Since K63 ubiquitination leads to increased protein stability and interactions, we tested whether inhibiting CDK2 would lead to functional effects in patient samples containing the SF3B1 mutation. Cases of MDS with SF3B1 mutations are characterized by ineffective hematopoiesis and treatment with CDK2 inhibitors in vitro led to increased hematopoietic colony formation and increased myeloid differentiation as evident by CD14 expression seen in three primary samples. ( Figure 3F).

IRAK4 inhibition leads to increased differentiation in SF3B1 mutant MDS
The hallmark of MDS and AML is a block in differentiation leading to dysplastic maturation and cytopenia. We wanted to evaluate the efficacy of IRAK4 inhibition in SF3B1 mutated MDS with a clinically relevant inhibitor, CA4948 (Emavusertib), which binds to the kinase domain on the protein ( Figure 4A). CA4948 treatment is able to inhibit downstream NF-kB activation upon TLR engagement in leukemic THP1 cells ( Figure 4B). This IRAK4 inhibitor also led to reduction in cytokine production in leukemic cells after engagements of Myd88/TLR pathways ( Figure 4C). Bone marrow mononuclear cells from MDS patients with SF3B1 mutation were cultured in the presence and absence of the IRAK4 inhibitor in vitro and then colonies were assessed by flow cytometry for myeloid differentiation. We observed a significant increase in myeloid colonies as well as increase in myeloid differentiation markers after IRAK4 inhibition in three distinct samples ( Figure 4D). Treatment of healthy human CD34 stem and progenitor cells with IRAK4 inhibitor did not lead to any significant changes in colony numbers ( Figure 4E). We also performed IRAK4 knockdown with siRNAs in a primary MDS sample with SF3B1 mutations and observed an increase in colonies and myeloid differentiation upon knockdown, validating IRAK4 as a target for relieving the differentiation block seen in MDS ( Figure 4F).

IRAK4 overexpression in MDS is associated with worse prognostic features and its inhibition leads to reduction in MDS/AML clones in vivo
Next we tested whether overexpression of IRAK4-long was associated with adverse clinical features in a large cohort of MDS samples (N=183) (Pellagatti et al., 2010). IRAK4-long expression was determined from an existing expression dataset from selected CD34+ cells and used to divide p65 were determined by immunoblotting. (C) Flag tagged plasmids of IRAK4-long isoform containing full length exon 6, IRAK4-S1, and IRAK4-S2 were transfected in HEK293T cells and treated with proteasome inhibitor MG132 (10 uM) overnight followed by immunoblotting. IRAK4-S1 and IRAK4-S2 constructs that led to smaller IRAK4 protein bands had lower protein expression and were accumulated upon proteasomal inhibition. (D) Ubiquitination of ectopic IRAK4 was determined in HEK-293T cells transfected with indicated plasmids in presence of MG132 (10 µM) by immunoprecipitating with HA-specific antibody and immunoblotting with Flag. (E) Immunoblotting analysis for indicated proteins in isogenic K562 cells with SF3B1 WT and K700E mutation. (F) HEK-293T cells were transfected with Flag tagged IRAK4 isoforms and HA tagged MYD88. MYD88 was immunoprecipitated with HAspecific antibody and its association with IRAK4 was probed by immunoblotting with specific antibodies.
The online version of this article includes the following source data and figure supplement(s) for figure 2: Source data 1. Expression levels of IRAKa and its signaling partners in HEK293T cells after transfection with indicated plasmids.
Source data 2. Expression levels of IRAK4 isforms transfected in HEK293T in presence of proteosome inhibitor.
Source data 3. Ubiquitination of ectopic IRAK4 in HEK293T in presence of MG132.

Source data 4. Expression levels of proteins in inflmmation pathway in K562 Ctl and K700E cells.
Source data 5. Association of IRAK4 isoforms with MyD88.    the cohort into high and low IRAK4 based on median expression. We observed that cohort with higher IRAK4-long expression had lower platelet counts, higher RBC transfusion needs and higher leukemic blast counts, all indicative of worse clinical features ( Figure 5A). Next, we established xenografts with MDS samples with SF3B1 mutation and treated the NSG mice with either IRAK4 inhibitor (CA4948) and placebo controls. Treatment with IRAK4 inhibitor led to decrease in MDS cells after 3-4 weeks of treatment in five distinct samples ( Figure 5B-D, Figure 5-figure supplement 1, Supplementary file 1).
Since innate immune signaling pathways have been shown to play roles in leukemic stem cell propagation , we next evaluated IRAK4-long expression in a dataset derived from sorted AML/MDS HSCs and HC (Barreyro et al., 2012). IRAK4-long was found to be elevated in selected cases of AML HSCs, most significantly in adverse risk patients with complex cytogenetics ( Figure 5E). Consistent with this finding, we derived a transcriptomic signature of IRAK4 overexpression from 183 MDS samples ( Figure 5-figure supplement 2) and compared it to a published leukemic stem cell signature (Eppert et al., 2011) and found significant concordance ( Figure 5F). Lastly, we tested the efficacy of IRAK4 inhibition in disease initiation in vivo. IRAK4 inhibitor and placebo treated xenograft mice were sacrificed and cells used for secondary xenografts. IRAK4 inhibition led to reduced MDS clones in secondary transplants, demonstrating decrease in disease initiating activity ( Figure 5G and H, Figure 5-figure supplement 3, summarized in Figure 5-figure supplement 4).

Discussion
MDS and AML are frequently incurable hematologic disorders characterized by cytopenia that are a major cause of morbidity and mortality. Mutations in splicing genes are one of the most common alterations in MDS and AML and predominantly affect SF3B1, U2AF1, and SRSF2 (1). SF3B1 is the most commonly mutated splicing gene in MDS and can also be mutated in solid tumors such as breast cancer, melanoma, and others (Papaemmanuil et al., 2013). Even though mutations in SF3B1 can affect splicing of numerous genes, the exact pathways that are disrupted by aberrant splicing and lead to ineffective hematopoiesis are not well elucidated. We recently demonstrated that mutant U2AF1 (S34F) directly regulates IRAK4 exon 4 retention in MDS/AML, which results in expression of the longer IRAK4 isoform (IRAK4-L) (Smith et al., 2019). In the present report, we demonstrate that and CDK2 peptide are shown in ribbon and surface representation, respectively. Positions P-2, P0, and P3 correspond to three distinct sub-pockets in the binding site, formed largely by residues Y473, M450, G472 (pink), A458, G470, K469 (yellow), and H376, V374, R392 (white). Capital lettering of conserved residues P, E, and W within the shown peptides depicts successful docking in corresponding sub-pockets. (C) Lys63-linked proteins were immunoprecipitated in K562 cells with wild type (WT) and K700E SF3B1 mutation and CDK2 ubiquitination was determined by CDK2 specific antibody. (D) Ubiquitination of CDK2 was determined in HEK-293T cells transfected with indicated plasmids by immunoprecipitating with HA specific antibody and immunoblotting with either Lys48 or Lys63 antibody. (E) Lys63-linked proteins were immunoprecipitated in HEK-293T cells transfected with indicated plasmids and CDK2 ubiquitination was determined by Myc-tag specific antibody. Sequence alignment of optimum amino-acids sequence predicted for TRAF6 with WT and mutant CDK2 (CDK2-122 and CDK2-123) (F-G) Myelodysplastic syndromes (MDS) patient derived samples (bone marrow/peripheral blood) were treated with DMSO or Dinaciclib (10 nM) or AT7519 (10 nM) for 14 days on methylcellulose clonogenic assays. The samples were evaluated for colony formation CFU-GM (colony forming unit -granulocyte macrophage) and for myeloid differentiation on colonies and were subjected to flow cytometry analysis.
The online version of this article includes the following source data and figure supplement(s) for figure 3: Source data 1. Ubiquitination of CDK2 in HEK293T cells.
Source data 2. Ubiquitination of CDK2 mutants in HEK293T cells.
Source data 3. Ubiquitination of CDK2 in K562 CTL and K700E cells.    SF3B1 mutations also lead to production of active IRAK4-L isoforms but by aberrant retention of exon 6 ( Figure 5-figure supplement 4). The functional activation of the IRAK4 pathway by two different exon retention events by U2AF1 mutations and SF3B1 mutations suggest that this is an important functional event in MDS pathobiology.
While IRAK4 and its downstream pathways have been studied in innate immunity and in the context of inflammatory diseases, its molecular and cellular effects in MDS/AML are relatively understudied. Recent work has shown that innate immune signaling mediators such as IL1RAP, IL8/CXCR2, TLRs as well as myeloid derived suppressor cells are activated in MDS (Barreyro et al., 2012;Dimicoli et al., 2013;Wei et al., 2013;Chen et al., 2013;Rhyasen et al., 2013). Our findings further support the role of activated innate immune signaling pathways in MDS and suggest that interaction of IRAK4 with upstream TLR/MyD88 pathways is critical in MDS pathogenesis. Demonstration of overexpression of active IRAK4 isoforms in splicing mutant MDS suggests that these cells are primed to respond to upstream TLR and MyD88 activation. In fact, since active IRAK4 isoforms are expressed in different spliceosome mutant subsets, they potentially represent a shared downstream functional pathway that regulates MDS/AML malignant cell survival.
CA-4948 is a specific inhibitor of IRAK4 that is now being tested in clinical trials in MDS/AML and lymphomas (ClinicalTrials.gov: NCT04278768; NCT03328078) and in preliminary findings has shown a tolerable safety profile. Early clinical data appears encouraging in spliceosome mutant patients, supporting the validity of IRAK4 as a therapeutic target in SF3B1 mutant MDS/AML (Garcia-Manero et al., 2021). CA4948 also shows inhibitory activity against FLT3 and CLKs, potentially another reason for the antimalignant activity observed in our preclinical studies. Our data suggests that inhibiting IRAK4 pharmacologically leads to shrinkage of the MDS/AML clones in vivo and this correlates with decreased blast counts seen with CA4948 monotherapy in relapsed/refractory MDS/AML in early results. MDS/AML are characterized by block in hematopoietic differentiation that leads to neutropenia and subsequent infections. Our data suggests that IRAK4 inhibition promotes myeloid differentiation in SF3B1 mutant cases. This affect can be therapeutically advantageous in clinical trials and supports further testing. Figure 5. IRAK4 overexpression is associated with adverse clinical features in human myelodysplastic syndromes (MDS), and its inhibition leads to reduction in MDS clones in vivo. (A) IRAK4 expression in 183 samples from MDS patient bone marrow CD34+ cells was correlated with platelet counts, RBC transfusion dependency, and blast counts. Cohort with high IRAK4 expression (>median) were associated with lower platelet counts, higher transfusion dependence, and higher blast counts (t-test, p<0.05). (B) NSG mice were xenografted with MDS cells with SF3B1 mutation. After

Additional information
Competing interests Molly A Smith: MS owns stock options in Poseida Therapeutics. The author has no other competing interests to declare. Robert N Booher: RB is a former employee of Curis, Inc, and holds patents patents related to CA-4948 (WO-2019089580-A1). RB also owns stocks in Curis, Inc. The author has no other competing interests to declare. Murali Ramachandra: MR is an employee of Aurigene Inc. Maria Samson: MS owns stock options in Curis, Inc as an employee. The author has no other competing interests to declare. Robert K Bradley: RKB is a named inventor on patent applications related to treating cancers with SF3B1 mutations filed by Fred Hutchinson Cancer Research Center (METHODS AND COMPOSITIONS COMPRISING BRD9 ACTIVATING THERAPIES FOR TREATING CANCERS AND RELATED DISORDERS -PCT/US2020/039645; SYNTHETIC INTRONS FOR TARGETED GENE EXPRESSION -PCT/US21/56273).The author has no other competing interests to declare. Robert E Martell: REM is an employee of Curis, Inc and has received honoria payments and also holds stocks in the company. The author has no other competing interests to declare. Ulrich G Steidl: US held grants from Bayer Healthcare and Aileron Therapauetics, and received consultancy fees from Aileron Therapeutics, Stelexis Therapeutics, Pieris Pharmaceuticals and Trillium Therapeutics. US is Director at Stelexis Therapeutics and holds stocks in the company. The author has no other competing interests to declare. Daniel T Starczynowski: DS received consultancy fees from Kurome Therapeutics, Treeline Biosciences, Tolero Therapeutics, and Captor Therapeutics. DS also owns stocks in Kurome Therapeutics. The author has no other competing interests to declare. Amit Verma: AV has received research funding from GlaxoSmithKline, BMS, Jannsen, Incyte, MedPacto, Celgene, Novartis, Curis, Prelude and Eli Lilly and Company, has received compensation as a scientific advisor to Novartis, Stelexis Therapeutics, Acceleron Pharma, and Celgene, and has equity ownership in Throws Exception and Stelexis Therapeutics. The other authors declare that no competing interests exist.  -4849, 12.5 mg/kg) and bone marrow aspirates were used to evaluate human cells engraftment by flow cytometry. (C-D) Summary of human cell engraftment for xenografted mice treated with either IRAK4 inhibitor (CA-4948) or vehicle. Representative MDS sample xenograft with SF3B1 K700E mutation is shown. (E) IRAK4 expression in sorted HSCs (Lineage -ve, CD34+, CD38−) cells from MDS/acute myeloid leukemia (AML) cases (N=9) and controls (N=5). Significantly increased IRAK4 expression is seen in samples with complex karyotype (CK) when compared to HC (t-test, p<0.05). (F) Gene expression signature associated with high IRAK4 expression (>median, from set of 183 MDS CD34+ cells) was compared to know leukemic stem cell signature and showed significant similarity by GSEA analysis. (G-H) MDS bone marrow cells were isolated and purified from mice either treated with vehicle or CA-4948 in and then xenografted in secondary recipient NSG mice. Human cell engraftment was evaluated after 4 weeks by flow cytometry on bone marrow aspirates.

Funding
The online version of this article includes the following figure supplement(s) for figure 5: